Forward Deployed Engineer (FDE) Master Program: GenAI, RAG, Agents & Enterprise AI – Live Training

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Forward Deployed Engineer (FDE) Master Program: GenAI, RAG, Agents & Enterprise AI – Live Training

(Generative AI, LLMs, Prompt Engineering, RAG, Vector Databases, AI Agents, Multi-Agent Systems, MCP, LangChain, LangGraph, AI Automation, Data Engineering, Cloud Architecture, AI-Assisted Development & Enterprise AI Delivery.)

The Forward Deployed Engineer (FDE) Master Program is a comprehensive, hands-on training designed to equip learners with the skills required to build, deploy, and deliver Enterprise AI solutions. This program covers the complete AI engineering lifecycle, including Generative AI (GenAI), Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), Embeddings, Vector Databases, AI Agents, Multi-Agent Systems, MCP (Model Context Protocol), LangChain, LangGraph, and AI Workflow Automation. Participants will learn how to select, customize, and optimize AI models, build intelligent applications, create advanced RAG pipelines, and develop agentic AI systems that solve real-world business challenges.

In addition to AI engineering, the course provides practical knowledge in Data Engineering, Cloud Architecture, AI-Assisted Software Development, AWS Services, Docker, CI/CD, Data Pipelines, ETL/ELT, Data Lakes, Lakehouses, and Enterprise AI Delivery Frameworks. Through real-time projects, labs, case studies, and industry scenarios, learners will gain expertise in translating business requirements into scalable AI solutions, building production-ready applications, automating workflows, and delivering measurable business value. By the end of the program, participants will be ready for roles such as Forward Deployed Engineer (FDE), AI Engineer, AI Solutions Architect, GenAI Engineer, Agentic AI Developer, AI Consultant, and Enterprise AI Specialist.

About the Instructor:

Tim Chowdary – Forward Deployed Engineering (FDE), Enterprise AI & Generative AI Expert

Tim Chowdary is a highly experienced technology professional with 19+ years of expertise in Enterprise AI, Forward Deployed Engineering (FDE), Data Engineering, Cloud Technologies, Digital Transformation, and Enterprise Solution Architecture. He specializes in helping organizations transform business challenges into scalable AI-powered solutions through a combination of technical expertise, consulting, and hands-on implementation.

With deep expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, Multi-Agent Systems, AI Automation, Data Engineering, Cloud Architecture, and Enterprise AI Delivery, Tim has successfully designed and delivered numerous enterprise-scale solutions across diverse industries.

As a Forward Deployed Engineer (FDE), Tim bridges the gap between business stakeholders and technical teams. His experience includes client discovery workshops, requirements gathering, solution architecture, rapid prototyping, proof-of-concept development, AI application deployment, and enterprise-scale implementation. He helps organizations identify high-value AI opportunities and transform them into practical, production-ready solutions.

Tim is also highly skilled in modern AI engineering practices, including Prompt Engineering, AI Agents, MCP (Model Context Protocol), AI Workflows, AI-Assisted Software Engineering, Enterprise RAG Systems, Cloud-Native AI Applications, and Data Platform Modernization. His practical approach enables organizations to accelerate AI adoption while maintaining scalability, security, and business alignment.

A passionate trainer and mentor, Tim has delivered 150+ training programs, workshops, and technical sessions, helping hundreds of professionals upskill in Forward Deployed Engineering (FDE), Enterprise AI, Data Engineering, Cloud Computing, and AI Solution Delivery. His training methodology focuses on real-world consulting scenarios, hands-on labs, industry use cases, and project-based learning.

Tim is widely recognized for simplifying complex technical concepts and making advanced AI technologies accessible to both technical and non-technical audiences. His sessions emphasize practical implementation, business value creation, and enterprise-ready solution design.

Through this Forward Deployed Engineering (FDE) program, Tim aims to equip participants with the skills needed to work directly with clients, design AI-driven solutions, build enterprise-grade applications, and successfully deliver modern AI transformation initiatives from concept to production.

Live Sessions Price:

For LIVE sessions – Offer price after discount is 3~~00 USD 259 ~~USD~~~~ 119 USD Or ~~13000 INR19900 INR 9~~900 Rupees

Enroll For Free Demo

Free Demo On:

Indian Timings: 22nd June @ 9 PM – 10 PM (IST)/

U.S Timings: 22nd June @ 11:30 AM – 12:30 PM (EST)/

U.K Timings: 22nd June @ 4:30 PM – 5:30 PM (BST)

Class Schedule:

For Participants in India: Every Monday, Wednesday, Friday & Saturday @ 9 PM – 10 PM (IST)/

For Participants in the US: Every Monday, Wednesday, Friday & Saturday @ 11:30 AM – 12:30 PM (EST)/

For Participants in the UK: Every Monday, Wednesday, Friday & Saturday @ 4:30 PM – 5:30 PM (BST)

What Our Students Say About the Trainer:

One of the best AI training programs I have attended. The trainer explained complex topics such as LLMs, Prompt Engineering, RAG, and AI Agents in a simple and practical manner. The labs and real-world use cases helped me gain confidence in applying these concepts. – Samiksha Lokhande

The Forward Deployed Engineer (FDE) program provided a perfect blend of theory and practical implementation. The sessions were highly interactive and focused on solving real business problems using modern AI technologies. I particularly enjoyed learning about AI Agents, Agentic Workflows, RAG architectures, and Enterprise AI Delivery. The trainer’s ability to connect concepts with real-world scenarios made the learning experience extremely valuable. This course has significantly improved my understanding of Enterprise AI and its practical applications. –Prathima

A great program for anyone looking to learn GenAI, RAG, and AI Agents from scratch. – Vivek Dhiran

Tim Chowdary’s teaching style is exceptional. He explains advanced AI concepts in a simple and structured way while sharing valuable industry insights. The course is packed with practical examples, hands-on exercises, and real-world use cases, making it highly beneficial for both beginners and experienced professionals. – Fatima Ali

The course content is well-structured and covers everything from Generative AI fundamentals to advanced RAG and Agentic AI concepts. The hands-on approach made learning easy and effective. – Michael Johnson

Prerequisites:

Basic understanding of Software Engineering, Programming, or Data Engineering concepts.
Familiarity with Python, APIs, Databases, or Cloud fundamentals is beneficial but not mandatory.
No prior AI experience is required; the course starts with Generative AI fundamentals and progresses to advanced topics through hands-on labs and projects.

Who can enroll for this course:

Software Engineers (Backend / Frontend / Full Stack)
DevOps Engineers
Cloud Engineers (AWS / Azure / GCP)
System Administrators
Technical Support / Application Support Engineers
QA / Test Engineers (Manual & Automation)
Technical Consultants
B.Tech / B.E (CSE, IT, ECE, etc.) students
B.Sc / BCA (Computer Science / IT) students
MCA / M.Tech students
Final-year students
Non-IT professionals with basic programming knowledge and IT understanding

Salient Features:

40+ Hours of Live Training along with recorded videos
1 Year Access to Session Recordings
Course Completion Certificate

What will I learn by the end of this course?

Understand Generative AI, LLMs, Prompt Engineering, and AI application development.
Build RAG (Retrieval-Augmented Generation) solutions using Embeddings and Vector Databases.
Develop AI Agents, Multi-Agent Systems, and AI-powered automation workflows.
Work with LangChain, LangGraph, MCP (Model Context Protocol), and AI orchestration frameworks.
Design and implement Data Pipelines, AI Workflows, and Enterprise AI solutions.
Use AI-assisted development tools for coding, testing, debugging, and rapid prototyping.
Deploy and manage AI applications using Cloud, Docker, and CI/CD practices.
Gain hands-on experience through labs, real-world use cases, and a mini project.
Learn how to translate business requirements into scalable Enterprise AI solutions.
Prepare for roles such as Forward Deployed Engineer (FDE), AI Engineer, GenAI Engineer, and AI Solutions Consultant.

Course Syllabus:

1. What is a Forward Deployed Engineer?

What is an FDE? | The intersection of engineering, consulting, and AI
FDE vs SWE | FDEs build *for* clients, SWEs build products
The FDE Mindset | Speed > Perfection · Empathy > Assumptions
FDE Skillset | AI + Data + Cloud + SE = the four pillars
Delivery Method | => Concepts, MCQs and Labs

2. AI & GenAI Foundations

What is Generative AI? | Models that *generate* content, not just classify
AI → ML → DL → GenAI | The evolution and where we are now
Transformer Architecture | Attention is all you need — simplifi ed mental model
Tokens & Tokenization | How LLMs see text; why it matters for cost & limits
Pre-training vs Fine-tuning vs RAG | Three ways to customize AI; when to use each
Open vs Closed Models | Tradeoff s: control vs convenience
AI Application Lifecycle | Idea → Prototype → Eval → Production → Monitor

3. LLM Fundamentals for FDEs

How LLMs Work | Predict the next token — that’s it (but the implications are enormous)
Context Window & Memory | The LLM’s “RAM” — what fi ts, what gets cut
Hallucination | Why LLMs make things up; mitigation strategies
Popular LLMs | Claude vs GPT-4o vs Gemini vs LLaMA — strengths & trade-off s
Model Selection for FDEs | Match model to task: cost, speed, quality triangle
API vs Self-Hosted | SaaS API for most; self-hosted for compliance/cost at scale
Cost & Latency | Token pricing, batching, caching strategies

4. Prompt Engineering for Enterprise FDEs

Prompt Engineering Overview | The #1 FDE superpower — 80% of AI quality comes from prompts
Zero-Shot / Few-Shot / CoT & Prompt Chaining | Know when to give examples, when to reason step-by-step
System Prompts & Role Design | Persona + Instructions + Constraints = reliable output
Prompt Chaining | Break complex tasks into sequential prompt steps
Structured Output (JSON Mode) | Force the LLM to return parseable data
Temperature & Controls | Creativity vs precision; when to dial each
Prompt Evaluation | A/B test prompts; measure output quality rigorously
Enterprise Patterns | Classifi cation, extraction, summarization, transformation

5. AI Engineering Core

Embeddings and Vector Databases
Chunking Strategies
Indexing Methods
Vector Databases
Vector DB Performance Optimization
What is RAG? Ground LLM responses in *your* data — eliminate hallucination
RAG vs Fine-Tuning, RAG for knowledge; fi ne-tuning for style/format
Data Ingestion, PDFs, databases, APIs, web pages → chunks → vectors
Retrieval Strategies ,Top-K, MMR (diversity), re-ranking
Metadata Filtering, Filter by date, category, author before vector search
Advanced RAG, Self-query, HyDE, Corrective RAG, RAPTOR
RAG Evaluation, Faithfulness, Answer Relevance, Context Recall (RAGAS)

6. AI Agents & Agent Orchestration

What is an Agent? | LLM + Tools + Memory + Decision Loop |
Tool Calling | Defi ne tools as JSON schema; LLM decides when/how to call them |
ReAct Pattern | Reason → Act → Observe → Repeat |
Agentic Design Patterns | Planning, Refl ection, Memory, Multi-step reasoning |
Agent Memory | Short-term (in-context), Long-term (DB), Episodic (past runs) |
Agent Observability | Trace every step; log tool calls; measure token usage |
Lab: Research Agent
“`python
# Build an agent that:
# 1. Accepts a research question
# 2. Searches the web (tool: web_search)
# 3. Reads relevant pages (tool: read_url)
# 4. Synthesizes fi ndings into a structured report
# 5. Saves output to a fi le (tool: write_fi le)

7. Multi-Agent Systems & AI Frameworks

Multi-Agent Architecture | Why one agent isn’t enough for complex enterprise tasks
Supervisor-Worker | Coordinator delegates; specialists execute
Peer Agents | Agents communicate directly; no central coordinator
LangChain | Building blocks: chains, memory, tools, retrievers
LangGraph | State machines for complex, stateful agent workfl ows
Agent State Sharing | Shared memory and message passing between agents

8. MCP, AI Workfl ows & Automation

What is MCP? | Standard protocol for LLMs to call external tools safely
MCP vs API | MCP is context-aware and AI-native; REST APIs are generic
MCP Components | Host (Claude Desktop), Client (app), Server (tool wrapper)
Building an MCP Server | Expose any tool (DB, API, fi le system) via MCP
AI Workfl ow Design | Directed graphs of AI steps + human checkpoints
Automation Patterns | Trigger-based, event-driven, scheduled AI workfl ows

9. AI-Assisted Software Engineering

AI-First SDLC | Prompt → Generate → Review → Refi ne → Ship (not: write → debug → repeat)
Claude Code | Agentic CLI that reads/writes fi les, runs commands, fi xes bugs
Cursor | AI-native IDE — inline generation, codebase-aware completions
GitHub Copilot | Autocomplete + chat — best for line-by-line acceleration
Rapid Prototyping | 0 → working demo in 2 hours using AI tools
AI Code Review | Use AI to review your own PRs before submitting
AI Debugging | “Here’s the error + stack trace — what’s wrong?”
Lab: Build a Full Feature in 2 Hours Using AI Tools**
- Challenge: Build a REST API with the following in under 2 hours using Claude Code:
- POST /analyze — accepts text, returns sentiment + entities + summary
- GET /history — returns last 20 analyses from SQLite
- GET /health — health check endpoint
- Full test suite (pytest)
- Docker containerization
- Rules: 3
- You may not manually write more than 20 lines of code
- All code must be generated or heavily assisted by AI
- You must review and understand every line before shipping

10. Data Engineering for FDEs

DE Fundamentals | Data is the fuel for AI — FDEs must be able to wrangle it
ETL vs ELT | Modern: load fi rst, transform in the warehouse (ELT wins)
Data Lake vs Lakehouse | Lakehouse = Delta Lake on object storage — best of both
Medallion Architecture | Bronze (raw) → Silver (cleaned) → Gold (business-ready)
Batch vs Streaming | Batch for history; streaming for real-time AI
Data Pipelines | Ingestion → transformation → loading → validation
Data Quality | Null checks, schema validation, freshness monitoring
Platform Landscape | Databricks, Snowfl ake, dbt, Airfl ow, Kafka — when to use each
AI + Data Integration | How AI pipelines consume data from your data platform
Lab: End-to-End Data Pipeline with AI Transformation**
- Scenario: Client has raw customer support tickets in S3 (CSVs).
- Goal: Build a pipeline that:
- Ingests raw CSVs to Bronze Delta table
- Cleans and deduplicates → Silver table
- Uses Claude to classify each ticket (category + priority) → Gold table
- Exposes Gold table via SQL for BI dashboard
- Stack: Python + pandas + delta-spark (local) + Claude API

11. Cloud Architecture for FDEs

Cloud-Native Principles | Design for failure; scale horizontally; automate everything
AWS Well-Architected | 5 pillars every FDE should know before touching client cloud
AI Platform Deployment | Where AI apps live: EC2, Lambda, ECS, Bedrock, SageMaker
Microservices & APIs | Decompose AI apps into independently deployable services
Docker for AI | Container your AI app → consistent, portable, deployable
Serverless for AI | Lambda for lightweight inference; cost-eff ective for event-driven AI
Security Fundamentals | IAM least-privilege; VPC isolation; secrets in AWS Secrets Manager
CI/CD for FDEs | GitHub Actions → test → build → deploy on every merge

12. Enterprise AI + FDE Delivery

Understanding the Forward Deployed Engineer (FDE) delivery model
Converting business problems into AI-driven technical solutions
Requirements gathering and stakeholder discovery techniques
Conducting effective client workshops and requirement analysis sessions
Problem Translation Framework for Enterprise AI projects
Designing AI solutions based on business objectives
Building rapid AI prototypes and proof-of-concepts (POCs)
Technical storytelling and solution presentation techniques
Enterprise AI project planning and delivery best practices
Communicating AI solutions to technical and non-technical stakeholders
Creating technical briefs, solution proposals, and implementation roadmaps
Portfolio building, case studies, GitHub projects, and LinkedIn branding
Real-world client engagement and consulting scenarios
End-to-end Enterprise AI solution delivery framework

13. Mini Project

Module 14: Watch Python videos for FREE here: (Self-paced recorded videos)

Click here to access and watch the Python videos

FAQs for the FDE (Forward Deployed Engineering) Course:

1. What is a Forward Deployed Engineer (FDE)?
An FDE is a professional who works directly with clients to understand business problems and build AI-powered solutions.

2. Who can join this course?
Anyone with a technical background such as Software Engineers, Test Engineers, Data Engineers, Cloud Engineers, or IT Professionals.

3. Do I need AI experience to join?
No. The course starts from the fundamentals and gradually moves to advanced topics.

4. What topics will be covered in this course?
Generative AI, LLMs, Prompt Engineering, RAG, AI Agents, MCP, Data Engineering, Cloud Architecture, and Enterprise AI Delivery.

5. Will there be practical hands-on sessions?
Yes. The course includes hands-on labs, exercises, real-world use cases, and a mini project.

6. Will I learn to build AI Agents?
Yes. You will learn how to build, manage, and deploy AI Agents and Multi-Agent Systems.

7. Which tools and technologies will be used?
Python, Claude, GPT, LangChain, LangGraph, Vector Databases, AWS concepts, GitHub Copilot, Cursor, and other modern AI tools.

8. Is this course suitable for working professionals?
Yes. The course is designed for both working professionals and aspiring AI engineers.

9. Will I get real-world project experience?
Yes. You will work on practical labs and a mini project based on enterprise AI scenarios.

10. What career opportunities can I explore after this course?
You can pursue roles such as Forward Deployed Engineer (FDE), AI Engineer, Generative AI Engineer, AI Consultant, Solution Architect, Data Engineer, or Enterprise AI Specialist.

How can I enroll for this course?

Enroll For Free Demo

For any other details, Call me or Whatsapp me on +91-9133190573

Live Sessions Price:

For LIVE sessions – Offer price after discount is 3~~00 USD 259 ~~USD~~~~ 109 USD Or ~~13000 INR19900 INR~~ 9900 Rupees

Sample Course Completion Certificate:

Your course completion certificate looks like this……

Note:

To maintain the quality of our training and ensure a smooth learning experience for all participants, we do not allow batch repetition or switching between courses.

To reiterate, moving from one course to another or shifting from one trainer to another (even if it is the same course) is not possible. Changing batches or trainers in any form is strictly not permitted.

We request all learners to attend the scheduled sessions regularly and make the most of their learning journey. Thank you for your understanding and continued support.